02yzoORjnaGi1WQDAQAKwNxc2uJDGpWF0rOOepgHzCIjejDQvuXFct_wy5KcgBCUEh0BbDyZg0FbXixebW1IEg

Face Relighting from a Single Image




3 views

Unformatted text preview:

Face Relighting from a Single Image under Arbitrary Unknown Lighting Conditions Yang Wang, Member, IEEE, Lei Zhang, Zicheng Liu, Senior Member, IEEE, Gang Hua, Member, IEEE, Zhen Wen, Zhengyou Zhang, Fellow, IEEE, and Dimitris Samaras, Member, IEEE Abstract—In this paper, we present a new method to modify the appearance of a face image by manipulating the illumination condition, when the face geometry and albedo information is unknown. This problem is particularly difficult when there is only a single image of the subject available. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using a spherical harmonic representation. Moreover, morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework by proposing a 3D spherical harmonic basis morphable model (SHBMM). The proposed method can represent a face under arbitrary unknown lighting and pose simply by three low-dimensional vectors, i.e., shape parameters, spherical harmonic basis parameters, and illumination coefficients, which are called the SHBMM parameters. However, when the image was taken under an extreme lighting condition, the approximation error can be large, thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion-based framework that uses a Markov random field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to extreme lighting conditions, but also insensitive to partial occlusions. The performance of our framework is demonstrated through various experimental results, including the improved rates for face recognition under extreme lighting conditions. Index Terms—Face synthesis and recognition, Markov random field, 3D spherical harmonic basis morphable model, vision for graphics. Ç 1 INTRODUCTION RECOVERING the geometry and texture of a human face from images remains a very important but challenging problem, with wide applications in both computer vision and computer graphics. One typical application is to generate photorealistic images of human faces under arbitrary lighting conditions [30], [9], [33], [12], [25], [24]. This problem is particularly difficult when there is only a single image of the subject available. Using spherical harmonic representation [2], [28], it has been shown that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated by a low-dimensional linear subspace. In this paper, we propose a new framework to estimate lighting, shape, and albedo of a human face from a single image, which can even be taken under extreme lighting conditions and/or with partial occlusions. The proposed method includes two parts. The first part is the 3D spherical harmonic basis morphable model (SHBMM), an integration of spherical harmonics into the morphable model frame- work. As a result, any face under arbitrary pose and illumination conditions can be represented simply by three low-dimensional vectors: shape parameters, spherical har- monic basis parameters, and illumination coefficients, which are called the SHBMM parameters. ...





Loading Unlocking...

Login

Join to view Face Relighting from a Single Image and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?

Sign Up

Join to view Face Relighting from a Single Image and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?